Common sense or censorship: How algorithmic moderators and message type influence perceptions of online content deletion

  • Joao Fernando Ferreira Goncalves (Creator)
  • Ina Weber (Erasmus School of Health Policy and Management (ESHPM), University of Antwerp) (Creator)
  • Gina M. Masullo (University of Texas at Austin) (Creator)
  • Marisa Torres da Silva (NOVA University Lisbon) (Creator)
  • Joep Hofhuis (Creator)

Dataset

Description

Hateful content online is a concern for social media platforms, policymakers, and the public. This has led high-profile content platforms, such as Facebook, to adopt algorithmic content-moderation systems; however, the impact of algorithmic moderation on user perceptions is unclear. We experimentally test the extent to which the type of content being removed (profanity vs hate speech) and the explanation given for its removal (no explanation vs link to community guidelines vs specific explanation) influence user perceptions of human and algorithmic moderators. Our preregistered study encompasses representative samples (N = 2870) from the United States, the Netherlands, and Portugal. Contrary to expectations, our findings suggest that algorithmic moderation is perceived as more transparent than human, especially when no explanation is given for content removal. In addition, sending users to community guidelines for further information on content deletion has negative effects on outcome fairness and trust.
Date made available2021

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